Multi-property response and optimisation of lightweight self-consolidating mortar containing silica fume and nano silica
Bibliographic record
Abstract
This article aims to investigate and optimise the properties of lightweight self-consolidating mortar (LSCM) by using the response surface method (RSM). Silica fume (SF), nano silica (NS), water-cement ratio (W/C) and superplasticiser (SP) were chosen as variables. Also, part of the aggregates was replaced with lightweight expanded clay aggregate (LECA). Due to the advantages of RSM in accommodating multi-response optimisation of LSCM properties, the investigation and analysis were carried out for the stability, segregation and compressive strength. The optimised results recommended acceptable ranges of the required rheological and hardened criteria for LSCM. The mixtures achieved in these ranges include an LSCM with sufficient stability and low segregation. In particular, the results showed that the ultimate segregation must be < 8% for structural concrete. In addition, the results proved that increasing the fraction of NS and SF improves the properties of the LSCM by reducing the segregation and increasing the stability and compressive strength. It was found that adding 6% of NS at a high W/C ratio decreased the segregation by at least 15%. Adding 6% NS and %SF to LSCM within 0.5 W/C increased the compressive strength up to 15.1 MPa for the former and 3.8 MPa for the latter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".